Configuring An Application For Execution On A Parallel Computer

ABSTRACT

Methods, systems, and products are disclosed for configuring an application for execution on a parallel computer that include: booting up a first subset of a plurality of nodes in a serial processing mode; booting up a second subset of the plurality of nodes in a parallel processing mode; profiling, prior to application deployment on the parallel computer, the application to identify the serial segments and the parallel segments of the application; and deploying the application for execution on the parallel computer in dependence upon the profile of the application and proximity within the data communications network of the nodes in the first subset relative to the nodes in the second subset.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation application of and claims priorityfrom U.S. patent application Ser. No. 12/109,259, filed on Apr. 24,2008.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The field of the invention is data processing, or, more specifically,methods, apparatus, and products for configuring an application forexecution on a parallel computer.

2. Description of Related Art

The development of the EDVAC computer system of 1948 is often cited asthe beginning of the computer era. Since that time, computer systemshave evolved into extremely complicated devices. Today's computers aremuch more sophisticated than early systems such as the EDVAC. Computersystems typically include a combination of hardware and softwarecomponents, application programs, operating systems, processors, buses,memory, input/output devices, and so on. As advances in semiconductorprocessing and computer architecture push the performance of thecomputer higher and higher, more sophisticated computer software hasevolved to take advantage of the higher performance of the hardware,resulting in computer systems today that are much more powerful thanjust a few years ago.

Parallel computing is an area of computer technology that hasexperienced advances. Parallel computing is the simultaneous executionof the same task (split up and specially adapted) on multiple processorsin order to obtain results faster. Parallel computing is based on thefact that the process of solving a problem usually can be divided intosmaller tasks, which may be carried out simultaneously with somecoordination.

Parallel computers execute parallel algorithms. A parallel algorithm canbe split up to be executed a piece at a time on many differentprocessing devices, and then put back together again at the end to get adata processing result. Some algorithms are easy to divide up intopieces. Splitting up the job of checking all of the numbers from one toa hundred thousand to see which are primes could be done, for example,by assigning a subset of the numbers to each available processor, andthen putting the list of positive results back together. In thisspecification, the multiple processing devices that execute theindividual pieces of a parallel program are referred to as ‘computenodes.’ A parallel computer is composed of compute nodes and otherprocessing nodes as well, including, for example, input/output (‘I/O’)nodes, and service nodes.

Parallel algorithms are valuable because it is faster to perform somekinds of large computing tasks via a parallel algorithm than it is via aserial (non-parallel) algorithm, because of the way modern processorswork. It is far more difficult to construct a computer with a singlefast processor than one with many slow processors with the samethroughput. There are also certain theoretical limits to the potentialspeed of serial processors. On the other hand, every parallel algorithmhas a serial part and so parallel algorithms have a saturation point.After that point adding more processors does not yield any morethroughput but only increases the overhead and cost.

Parallel algorithms are designed also to optimize one more resource thedata communications requirements among the nodes of a parallel computer.There are two ways parallel processors communicate, shared memory ormessage passing. Shared memory processing needs additional locking forthe data and imposes the overhead of additional processor and bus cyclesand also serializes some portion of the algorithm.

Message passing processing uses high-speed data communications networksand message buffers, but this communication adds transfer overhead onthe data communications networks as well as additional memory need formessage buffers and latency in the data communications among nodes.Designs of parallel computers use specially designed data communicationslinks so that the communication overhead will be small but it is theparallel algorithm that decides the volume of the traffic.

Many data communications network architectures are used for messagepassing among nodes in parallel computers. Compute nodes may beorganized in a network as a ‘torus’ or ‘mesh,’ for example. Also,compute nodes may be organized in a network as a tree. A torus networkconnects the nodes in a three-dimensional mesh with wrap around links.Every node is connected to its six neighbors through this torus network,and each node is addressed by its x,y,z coordinate in the mesh. In atree network, the nodes typically are connected into a binary tree: eachnode has a parent, and two children (although some nodes may only havezero children or one child, depending on the hardware configuration). Incomputers that use a torus and a tree network, the two networkstypically are implemented independently of one another, with separaterouting circuits, separate physical links, and separate message buffers.

A torus network generally supports point-to-point communications. A treenetwork, however, typically only supports communications where data fromone compute node migrates through tiers of the tree network to a rootcompute node or where data is multicast from the root to all of theother compute nodes in the tree network. In such a manner, the treenetwork lends itself to collective operations such as, for example,reduction operations or broadcast operations. In the current art,however, the tree network does not lend itself to and is typicallyinefficient for point-to-point operations. Although in general the torusnetwork and the tree network are each optimized for certaincommunications patterns, those communications patterns may be supportedby either network.

Many parallel computers consist of compute nodes that each only supportsa single thread. Such parallel computers are sufficient for processing aparallel application in which the application consists of instructionsthat are only executed serially on each compute node using a singlethread. To further enhance performance, however, more robust parallelcomputers include compute nodes that each supports multiple threadsusing a multi-processor architecture. Using these more robust parallelcomputers, software engineers have developed parallel applications inwhich each application consists of segments of instructions that areonly executed serially on each node using a single thread and othersegments that may be executed in parallel on each node using multiplethreads. That is, each compute node utilizes a single processor whileexecuting the serial code segments and spawns threads to the otherprocessors on that node while executing the parallel code segments. Thedrawback to executing multi-threaded parallel applications on these morerobust parallel computers in such a manner is that computing resourcesare being underutilized when the compute nodes are executing the serialcode segments. As mentioned above, when the compute nodes are executingthe serial code segments, each compute node is processing only a singlethread, and thereby only utilizing a single processor in itsmulti-processor architecture.

SUMMARY OF THE INVENTION

Methods, systems, and products are disclosed for configuring anapplication for execution on a parallel computer. The parallel computerincludes a plurality of compute nodes connected together through a datacommunications network. Each compute node has a plurality of processorscapable of operating independently for serial processing among theprocessors and capable of operating symmetrically for parallelprocessing among the processors. The application has parallel segmentsdesignated for parallel processing and serial segments designated forserial processing. Configuring an application for execution on aparallel computer includes: booting up a first subset of the pluralityof compute nodes in a serial processing mode; booting up a second subsetof the plurality of compute nodes in a parallel processing mode;profiling, prior to application deployment on the parallel computer, theapplication to identify the serial segments and the parallel segments ofthe application; and deploying the application for execution on theparallel computer in dependence upon the profile of the application andproximity within the data communications network of the compute nodes inthe first subset relative to the compute nodes in the second subset.

The foregoing and other objects, features and advantages of theinvention will be apparent from the following more particulardescriptions of exemplary embodiments of the invention as illustrated inthe accompanying drawings wherein like reference numbers generallyrepresent like parts of exemplary embodiments of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary parallel computer for configuring anapplication for execution on a parallel computer according toembodiments of the present invention.

FIG. 2 sets forth a block diagram of an exemplary compute node useful ina parallel computer capable of configuring an application for executionon a parallel computer according to embodiments of the presentinvention.

FIG. 3A illustrates an exemplary Point To Point Adapter useful in aparallel computer capable of configuring an application for execution ona parallel computer according to embodiments of the present invention.

FIG. 3B illustrates an exemplary Global Combining Network Adapter usefulin a parallel computer capable of configuring an application forexecution on a parallel computer according to embodiments of the presentinvention.

FIG. 4 sets forth a line drawing illustrating an exemplary datacommunications network optimized for point to point operations useful ina parallel computer capable of configuring an application for executionon a parallel computer according to embodiments of the presentinvention.

FIG. 5 sets forth a line drawing illustrating an exemplary datacommunications network optimized for collective operations useful in aparallel computer capable of configuring an application for execution ona parallel computer according to embodiments of the present invention.

FIG. 6 sets forth a line drawing illustrating an exemplary parallelcomputer on which an application is configured for execution accordingto embodiments of the present invention.

FIG. 7 sets forth a line drawing illustrating a further exemplaryparallel computer on which an application is configured for executionaccording to embodiments of the present invention.

FIG. 8 sets forth a flow chart illustrating an exemplary method forconfiguring an application for execution on a parallel computeraccording to the present invention.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Exemplary methods, apparatus, and computer program products forconfiguring an application for execution on a parallel computeraccording to embodiments of the present invention are described withreference to the accompanying drawings, beginning with FIG. 1. FIG. 1illustrates an exemplary parallel computer for configuring anapplication for execution on a parallel computer according toembodiments of the present invention. The system of FIG. 1 includes aparallel computer (100), non-volatile memory for the computer in theform of data storage device (118), an output device for the computer inthe form of printer (120), and an input/output device for the computerin the form of computer terminal (122). Parallel computer (100) in theexample of FIG. 1 includes a plurality of compute nodes (102).

Each compute node (102) includes a plurality of processors for use inexecuting an application on the parallel computer (100) according toembodiments of the present invention. The processors of each computenode (102) in FIG. 1 are operatively coupled to computer memory such as,for example, random access memory (‘RAM’). Each compute node (102) mayoperate in several distinct modes that affect the relationship among theprocessors and the memory on that node such as, for example, serialprocessing mode and parallel processing mode. The mode in which thecompute nodes operate is generally set during the node's boot processand does not change until the node reboots.

In a serial processing mode, often referred to a ‘virtual node mode,’the processors of a compute node operate independently of one another,and each processor has access to a partition of the node's memory thatis exclusively dedicated to that processor. For example, if a computenode has four processors and two Gigabytes (GB) of RAM, when operatingin serial processing mode, each processor may process a threadindependently of the other processors on that node, and each processormay access a portion of that node's 2GB of RAM.

In a parallel processing mode, often referred to as ‘symmetricmultiprocessing mode,’ one of the processors acts as a master, and theremaining processors serve as slaves to the master processor. Eachprocessor has access to the full range of computer memory on the computenode. Continuing with the exemplary node above having four processorsand 2GB of RAM, for example, each slave processor may cooperativelyprocess threads spawned from the master processor, and all of theprocessors have access to the node's entire 2GB of RAM.

In the parallel computer (100) of FIG. 1, the compute nodes (102) arecoupled for data communications by several independent datacommunications networks including a Joint Test Action Group (‘JTAG’)network (104), a global combining network (106) which is optimized forcollective operations, and a torus network (108) which is optimizedpoint to point operations. The global combining network (106) is a datacommunications network that includes data communications links connectedto the compute nodes so as to organize the compute nodes as a tree. Eachdata communications network is implemented with network links among thecompute nodes (102). The network links provide data communications forparallel operations among the compute nodes of the parallel computer.The links between compute nodes are bi-directional links that aretypically implemented using two separate directional data communicationspaths.

In addition, the compute nodes (102) of parallel computer are organizedinto at least one operational group (132) of compute nodes forcollective parallel operations on parallel computer (100). Anoperational group of compute nodes is the set of compute nodes uponwhich a collective parallel operation executes. Collective operationsare implemented with data communications among the compute nodes of anoperational group. Collective operations are those functions thatinvolve all the compute nodes of an operational group. A collectiveoperation is an operation, a message-passing computer programinstruction that is executed simultaneously, that is, at approximatelythe same time, by all the compute nodes in an operational group ofcompute nodes. Such an operational group may include all the computenodes in a parallel computer (100) or a subset all the compute nodes.Collective operations are often built around point to point operations.A collective operation requires that all processes on all compute nodeswithin an operational group call the same collective operation withmatching arguments. A ‘broadcast’ is an example of a collectiveoperation for moving data among compute nodes of an operational group. A‘reduce’ operation is an example of a collective operation that executesarithmetic or logical functions on data distributed among the computenodes of an operational group. An operational group may be implementedas, for example, an MPI ‘communicator.’

‘MPI’ refers to ‘Message Passing Interface,’ a prior art parallelcommunications library, a module of computer program instructions fordata communications on parallel computers. Examples of prior-artparallel communications libraries that may be improved for use withsystems according to embodiments of the present invention include MPIand the ‘Parallel Virtual Machine’ (‘PVM’) library. PVM was developed bythe University of Tennessee, The Oak Ridge National Laboratory, andEmory University. MPI is promulgated by the MPI Forum, an open groupwith representatives from many organizations that define and maintainthe MPI standard. MPI at the time of this writing is a de facto standardfor communication among compute nodes running a parallel program on adistributed memory parallel computer. This specification sometimes usesMPI terminology for ease of explanation, although the use of MPI as suchis not a requirement or limitation of the present invention.

Some collective operations have a single originating or receivingprocess running on a particular compute node in an operational group.For example, in a ‘broadcast’ collective operation, the process on thecompute node that distributes the data to all the other compute nodes isan originating process. In a ‘gather’ operation, for example, theprocess on the compute node that received all the data from the othercompute nodes is a receiving process. The compute node on which such anoriginating or receiving process runs is typically referred to as alogical root.

Most collective operations are variations or combinations of four basicoperations: broadcast, gather, scatter, and reduce. The interfaces forthese collective operations are defined in the MPI standards promulgatedby the MPI Forum. Algorithms for executing collective operations,however, are not defined in the MPI standards. In a broadcast operation,all processes specify the same root process, whose buffer contents willbe sent. Processes other than the root specify receive buffers. Afterthe operation, all buffers contain the message from the root process.

In a scatter operation, the logical root divides data on the root intosegments and distributes a different segment to each compute node in theoperational group. In scatter operation, all processes typically specifythe same receive count. The send arguments are only significant to theroot process, whose buffer actually contains sendcount*N elements of agiven data type, where N is the number of processes in the given groupof compute nodes. The send buffer is divided and dispersed to allprocesses (including the process on the logical root). Each compute nodeis assigned a sequential identifier termed a ‘rank.’ After theoperation, the root has sent sendcount data elements to each process inincreasing rank order. Rank 0 receives the first sendcount data elementsfrom the send buffer. Rank 1 receives the second sendcount data elementsfrom the send buffer, and so on.

A gather operation is a many-to-one collective operation that is acomplete reverse of the description of the scatter operation. That is, agather is a many-to-one collective operation in which elements of adatatype are gathered from the various processes running on the rankedcompute nodes into a receive buffer in a root node.

A reduce operation is also a many-to-one collective operation thatincludes an arithmetic or logical function performed on two dataelements. All processes specify the same ‘count’ and the same arithmeticor logical function. After the reduction, all processes have sent countdata elements from computer node send buffers to the root process. In areduction operation, data elements from corresponding send bufferlocations are combined pair-wise by arithmetic or logical operations toyield a single corresponding element in the root process's receivebuffer. Application specific reduction operations can be defined atruntime. Parallel communications libraries may support predefinedoperations. MPI, for example, provides the following pre-definedreduction operations:

-   -   MPI_MAX maximum    -   MPI_MIN minimum    -   MPI_SUM sum    -   MPI_PROD product    -   MPI_LAND logical and    -   MPI_BAND bitwise and    -   MPI_LOR logical or    -   MPI_BOR bitwise or    -   MPI_LXOR logical exclusive or    -   MPI_BXOR bitwise exclusive or

As mentioned above, most collective operation communications patternsbuild off of these basic collective operations. One such communicationspattern is a gossiping communications pattern in which one set ofcompute nodes communicates with another set of compute nodes. The twosets of nodes participating in the gossip communications pattern couldbe the same or different. Examples of gossiping communications patternsimplemented using MPI may include an all-to-all operation, anall-to-ally operation, an allgather operation, an allgathery operation,and so on.

In addition to compute nodes, the parallel computer (100) includesinput/output (‘I/O’) nodes (110, 114) coupled to compute nodes (102)through the global combining network (106). The I/O nodes (110, 114)provide I/O services between compute nodes (102) and I/O devices (118,120, 122). I/O nodes (110, 114) are connected for data communicationsI/O devices (118, 120, 122) through local area network (‘LAN’) (130)implemented using high-speed Ethernet. The parallel computer (100) alsoincludes a service node (116) coupled to the compute nodes through oneof the networks (104). Service node (116) provides services common topluralities of compute nodes, administering the configuration of computenodes, loading programs into the compute nodes, starting programexecution on the compute nodes, retrieving results of program operationson the computer nodes, and so on. Service node (116) runs a serviceapplication (124) and communicates with users (128) through a serviceapplication interface (126) that runs on computer terminal (122).

As described in more detail below in this specification, the servicenode (116) of FIG. 1 in the parallel computer (100) operates generallyfor configuring an application for execution on a parallel computeraccording to embodiments of the present invention. The application iscomputer software having parallel segments designated for parallelprocessing on each compute node and serial segments designated forserial processing on each compute node. The service node (116) of FIG. 1operates generally for configuring an application for execution on aparallel computer according to embodiments of the present invention by:booting up a first subset of the plurality of compute nodes (102) in aserial processing mode; booting up a second subset of the plurality ofcompute nodes (102) in a parallel processing mode; profiling, prior toapplication deployment on the parallel computer (100), the applicationto identify the serial segments and the parallel segments of theapplication; and deploying the application for execution on the parallelcomputer (100) in dependence upon the profile of the application andproximity within the data communications network of the compute nodes(102) in the first subset relative to the compute nodes in the secondsubset.

Readers will note that the term ‘booting’ as applied to compute nodesgenerally refers to the process of initializing compute node componentsto prepare the compute node for executing application layer software.Such booting may occur when power is first applied to each compute node,when power is cycled to each compute node, or when certain reset valuesare written to component registers. The process of booting a computenode may include loading system layer software such as an operatingsystem to provide an interface through which application layer softwaremay access the node's hardware. Such system layer software however maybe quite lightweight by comparison with system layer software of generalpurpose computers. That is, such system layer software may be a pareddown version as it were of system layer software developed for generalpurpose computers.

The arrangement of nodes, networks, and I/O devices making up theexemplary system illustrated in FIG. 1 are for explanation only, not forlimitation of the present invention. Data processing systems capable ofconfiguring an application for execution on a parallel computeraccording to embodiments of the present invention may include additionalnodes, networks, devices, and architectures, not shown in FIG. 1, aswill occur to those of skill in the art. Although the parallel computer(100) in the example of FIG. 1 includes sixteen compute nodes (102),readers will note that parallel computers capable of determining when aset of compute nodes participating in a barrier operation are ready toexit the barrier operation according to embodiments of the presentinvention may include any number of compute nodes. In addition toEthernet and JTAG, networks in such data processing systems may supportmany data communications protocols including for example TCP(Transmission Control Protocol), IP (Internet Protocol), and others aswill occur to those of skill in the art. Various embodiments of thepresent invention may be implemented on a variety of hardware platformsin addition to those illustrated in FIG. 1.

Configuring an application for execution on a parallel computeraccording to embodiments of the present invention may be generallyimplemented on a parallel computer that includes a plurality of computenodes. In fact, such computers may include thousands of such computenodes. Each compute node is in turn itself a kind of computer composedof a plurality of computer processors (or processing cores), its owncomputer memory, and its own input/output adapters. For furtherexplanation, therefore, FIG. 2 sets forth a block diagram of anexemplary compute node useful in a parallel computer capable ofconfiguring an application for execution on a parallel computeraccording to embodiments of the present invention. The compute node(152) of FIG. 2 includes a plurality of processors (164) as well asrandom access memory (‘RAM’) (156). The processors (164) are connectedto RAM (156) through a high-speed memory bus (154) and through a busadapter (194) and an extension bus (168) to other components of thecompute node (152).

Stored in RAM (156) is an application program (158), a module ofcomputer program instructions that carries out parallel, user-level dataprocessing using parallel algorithms. The application (158) of FIG. 2has both parallel segments designated for parallel processing on eachcompute node and serial segments designated for serial processing oneach compute node. The serial segments are portions of the application(158) that include computer program instructions for execution seriallyin a single thread on the compute node. The parallel segments areportions of the application (158) that include computer programinstructions for execution in parallel on the compute node usingmultiple threads—typically one thread per processor (164).

Also stored in RAM (156) is a messaging module (160), a library ofcomputer program instructions that carry out parallel communicationsamong compute nodes, including point to point operations as well ascollective operations. Application program (158) executes collectiveoperations by calling software routines in the messaging module (160). Alibrary of parallel communications routines may be developed fromscratch for use in systems according to embodiments of the presentinvention, using a traditional programming language such as the Cprogramming language, and using traditional programming methods to writeparallel communications routines that send and receive data among nodeson two independent data communications networks. Alternatively, existingprior art libraries may be improved to operate according to embodimentsof the present invention. Examples of prior-art parallel communicationslibraries include the ‘Message Passing Interface’ (‘MPI’) library andthe ‘Parallel Virtual Machine’ (‘PVM’) library.

Also stored in RAM (156) is a multi-processing module (161), a libraryof computer program instructions that carry out shared memorymulti-processing among the plurality of processors (164) on the computenode (152). Application program (158) executes shared memorymulti-processing operations using the functionality provided by themulti-processing module (161). The multi-processing module (161) mayimplement functionality specified in various shared memorymulti-processing platforms such as, for example, the OpenMP™ sharedmemory multi-processing platform. Although illustrated in FIG. 2 as aseparate component, readers will recognize that the multi-processingmodule (161) of FIG. 2 may be implemented as a component of an operatingsystem.

Also stored in RAM (156) is an operating system (162), a module ofcomputer program instructions and routines for an application program'saccess to other resources of the compute node. The operating system(162) may be quite lightweight by comparison with operating systems ofgeneral purpose computers, a pared down version as it were, or anoperating system developed specifically for operations on a particularparallel computer. Operating systems that may usefully be improved,simplified, for use in a compute node include UNIX™, Linux™, MicrosoftXP™, AIX™, IBM's i5/OS™, and others as will occur to those of skill inthe art.

Although the operating system (162) generally controls execution of theapplication (158) in the example of FIG. 2, the operating system (162)also includes a migration manager (200), which is a set of computerprogram instructions for migrating the application (158) from onecompute node to another based on the segments of the application (158).The migration manager (200) may communicate with the migration manageron other compute nodes and with the service node to determine whetherthe application (158) should be migrated to another node, the node towhich the migration manager (200) should migrate the application (158),the extent of the migration, and so on.

The exemplary compute node (152) of FIG. 2 includes severalcommunications adapters (172, 176, 180, 188) for implementing datacommunications with other nodes of a parallel computer. Such datacommunications may be carried out serially through RS-232 connections,through external buses such as Universal Serial Bus (USW), through datacommunications networks such as IP networks, and in other ways as willoccur to those of skill in the art. Communications adapters implementthe hardware level of data communications through which one computersends data communications to another computer, directly or through anetwork. Examples of communications adapters useful in systems forconfiguring an application for execution on a parallel computeraccording to embodiments of the present invention include modems forwired communications, Ethernet (IEEE 802.3) adapters for wired networkcommunications, and 802.11b adapters for wireless networkcommunications.

The data communications adapters in the example of FIG. 2 include aGigabit Ethernet adapter (172) that couples example compute node (152)for data communications to a Gigabit Ethernet (174). Gigabit Ethernet isa network transmission standard, defined in the IEEE 802.3 standard,that provides a data rate of 1 billion bits per second (one gigabit).Gigabit Ethernet is a variant of Ethernet that operates over multimodefiber optic cable, single mode fiber optic cable, or unshielded twistedpair.

The data communications adapters in the example of FIG. 2 includes aJTAG Slave circuit (176) that couples example compute node (152) fordata communications to a JTAG Master circuit (178). JTAG is the usualname used for the IEEE 1149.1 standard entitled Standard Test AccessPort and Boundary-Scan Architecture for test access ports used fortesting printed circuit boards using boundary scan. JTAG is so widelyadapted that, at this time, boundary scan is more or less synonymouswith JTAG. JTAG is used not only for printed circuit boards, but alsofor conducting boundary scans of integrated circuits, and is also usefulas a mechanism for debugging embedded systems, providing a convenient“back door” into the system. The example compute node of FIG. 2 may beall three of these: It typically includes one or more integratedcircuits installed on a printed circuit board and may be implemented asan embedded system having its own processor, its own memory, and its ownI/O capability. JTAG boundary scans through JTAG Slave (176) mayefficiently configure processor registers and memory in compute node(152) for use in configuring an application for execution on a parallelcomputer according to embodiments of the present invention.

The data communications adapters in the example of FIG. 2 includes aPoint To Point Adapter (180) that couples example compute node (152) fordata communications to a network (108) that is optimal for point topoint message passing operations such as, for example, a networkconfigured as a three-dimensional torus or mesh. Point To Point Adapter(180) provides data communications in six directions on threecommunications axes, x, y, and z, through six bidirectional links: +x(181), −x (182), +y (183), −y (184), +z (185), and −z (186).

The data communications adapters in the example of FIG. 2 includes aGlobal Combining Network Adapter (188) that couples example compute node(152) for data communications to a network (106) that is optimal forcollective message passing operations on a global combining networkconfigured, for example, as a binary tree. The Global Combining NetworkAdapter (188) provides data communications through three bidirectionallinks: two to children nodes (190) and one to a parent node (192).

Example compute node (152) includes two arithmetic logic units (‘ALUs’).ALU (166) is a component of each processing core (164), and a separateALU (170) is dedicated to the exclusive use of Global Combining NetworkAdapter (188) for use in performing the arithmetic and logical functionsof reduction operations. Computer program instructions of a reductionroutine in parallel communications library (160) may latch aninstruction for an arithmetic or logical function into instructionregister (169). When the arithmetic or logical function of a reductionoperation is a ‘sum’ or a ‘logical or,’ for example, Global CombiningNetwork Adapter (188) may execute the arithmetic or logical operation byuse of ALU (166) in processor (164) or, typically much faster, by usededicated ALU (170).

The example compute node (152) of FIG. 2 includes a direct memory access(DMA') controller (195), which is computer hardware for direct memoryaccess and a DMA engine (197), which is computer software for directmemory access. In the example of FIG. 2, the DMA engine (197) isconfigured in computer memory of the DMA controller (195). Direct memoryaccess includes reading and writing to memory of compute nodes withreduced operational burden on the central processing units (164). A DMAtransfer essentially copies a block of memory from one location toanother, typically from one compute node to another. While the CPU mayinitiate the DMA transfer, the CPU does not execute it.

The exemplary compute node (152) of FIG. 2 is included in a parallelcomputer that operates generally for configuring the application (158)for execution on a parallel computer according to embodiments of thepresent invention. Such a parallel computer operates generally forconfiguring the application (158) for execution on a parallel computeraccording to embodiments of the present invention by: booting up a firstsubset of the plurality of compute nodes in a serial processing mode;booting up a second subset of the plurality of compute nodes in aparallel processing mode; profiling, prior to application deployment onthe parallel computer, the application to identify the serial segmentsand the parallel segments of the application; and deploying theapplication for execution on the parallel computer in dependence uponthe profile of the application and proximity within the datacommunications network of the compute nodes in the first subset relativeto the compute nodes in the second subset.

For further explanation, FIG. 3A illustrates an exemplary Point To PointAdapter (180) useful in a parallel computer capable of configuring anapplication for execution on a parallel computer according toembodiments of the present invention. Point To Point Adapter (180) isdesigned for use in a data communications network optimized for point topoint operations, a network that organizes compute nodes in athree-dimensional torus or mesh. Point To Point Adapter (180) in theexample of FIG. 3A provides data communication along an x-axis throughfour unidirectional data communications links, to and from the next nodein the −x direction (182) and to and from the next node in the +xdirection (181). Point To Point Adapter (180) also provides datacommunication along a y-axis through four unidirectional datacommunications links, to and from the next node in the −y direction(184) and to and from the next node in the +y direction (183). Point ToPoint Adapter (180) in FIG. 3A also provides data communication along az-axis through four unidirectional data communications links, to andfrom the next node in the −z direction (186) and to and from the nextnode in the +z direction (185).

For further explanation, FIG. 3B illustrates an exemplary GlobalCombining Network Adapter (188) useful in a parallel computer capable ofbroadcasting collective operation contributions throughout the parallelcomputer according to embodiments of the present invention. GlobalCombining Network Adapter (188) is designed for use in a networkoptimized for collective operations, a network that organizes computenodes of a parallel computer in a binary tree. Global Combining NetworkAdapter (188) in the example of FIG. 3B provides data communication toand from two children nodes (190) through two links. Each link to eachchild node (190) is formed from two unidirectional data communicationspaths. Global Combining Network Adapter (188) also provides datacommunication to and from a parent node (192) through a link formed fromtwo unidirectional data communications paths.

For further explanation, FIG. 4 sets forth a line drawing illustratingan exemplary data communications network (108) optimized for point topoint operations useful in a parallel computer capable of configuring anapplication for execution on a parallel computer in accordance withembodiments of the present invention. In the example of FIG. 4, dotsrepresent compute nodes (102) of a parallel computer, and the dottedlines between the dots represent data communications links (103) betweencompute nodes. The data communications links are implemented with pointto point data communications adapters similar to the one illustrated forexample in FIG. 3A, with data communications links on three axes, x, y,and z, and to and from in six directions +x (181), −x (182), +y (183),−y (184), +z (185), and −z (186). The links and compute nodes areorganized by this data communications network optimized for point topoint operations into a three dimensional mesh (105). The mesh (105) haswrap-around links on each axis that connect the outermost compute nodesin the mesh (105) on opposite sides of the mesh (105). These wrap-aroundlinks form part of a torus (107). Each compute node in the torus has alocation in the torus that is uniquely specified by a set of x, y, zcoordinates. Readers will note that the wrap-around links in the y and zdirections have been omitted for clarity, but are configured in asimilar manner to the wrap-around link illustrated in the x direction.For clarity of explanation, the data communications network of FIG. 4 isillustrated with only 27 compute nodes, but readers will recognize thata data communications network optimized for point to point operationsfor use in configuring an application for execution on a parallelcomputer in accordance with embodiments of the present invention maycontain only a few compute nodes or may contain thousands of computenodes.

For further explanation, FIG. 5 sets forth a line drawing illustratingan exemplary data communications network (106) optimized for collectiveoperations useful in a parallel computer capable of configuring anapplication for execution on a parallel computer in accordance withembodiments of the present invention. The example data communicationsnetwork of FIG. 5 includes data communications links connected to thecompute nodes so as to organize the compute nodes as a tree. In theexample of FIG. 5, dots represent compute nodes (102) of a parallelcomputer, and the dotted lines (103) between the dots represent datacommunications links between compute nodes. The data communicationslinks are implemented with global combining network adapters similar tothe one illustrated for example in FIG. 3B, with each node typicallyproviding data communications to and from two children nodes and datacommunications to and from a parent node, with some exceptions. Nodes ina binary tree (106) may be characterized as a physical root node (202),branch nodes (204), and leaf nodes (206). The root node (202) has twochildren but no parent. The leaf nodes (206) each has a parent, but leafnodes have no children. The branch nodes (204) each has both a parentand two children. The links and compute nodes are thereby organized bythis data communications network optimized for collective operationsinto a binary tree (106). For clarity of explanation, the datacommunications network of FIG. 5 is illustrated with only 31 computenodes, but readers will recognize that a data communications networkoptimized for collective operations for use in a parallel computer forconfiguring an application for execution on a parallel computer inaccordance with embodiments of the present invention may contain only afew compute nodes or may contain thousands of compute nodes.

In the example of FIG. 5, each node in the tree is assigned a unitidentifier referred to as a ‘rank’ (250). A node's rank uniquelyidentifies the node's location in the tree network for use in both pointto point and collective operations in the tree network. The ranks inthis example are assigned as integers beginning with 0 assigned to theroot node (202), 1 assigned to the first node in the second layer of thetree, 2 assigned to the second node in the second layer of the tree, 3assigned to the first node in the third layer of the tree, 4 assigned tothe second node in the third layer of the tree, and so on. For ease ofillustration, only the ranks of the first three layers of the tree areshown here, but all compute nodes in the tree network are assigned aunique rank.

For further explanation, FIG. 6 sets forth a line drawing illustratingan exemplary parallel computer (100) on which an application (158) isconfigured for execution according to embodiments of the presentinvention. The parallel computer (100) of FIG. 6 includes sixteencompute nodes labeled 0-15 and connected together through a datacommunications network. The compute nodes are spread across four rackslabeled 0-3. Rack 0 includes nodes 0-3, rack 1 includes nodes 4-7, rack2 includes 8-11, and rack 3 includes nodes 12-15. Due to the physicallinks in the data communications network connecting the nodes in eachrack 0-3, the nodes in rack 0 are directly connected to the nodes inrack 1, the nodes in rack 1 are directly connected to the nodes in rack2, and the nodes in rack 2 are directly connected to the nodes in rack3. All other data communication connections between racks 0-3 areindirect and require traversal of an intervening rack. For example, datacommunications between the nodes in rack 0 and the nodes in rack 2 mustpass through rack 1 in the example of FIG. 6. Readers will note,however, that the network topology of the nodes in the example of FIG. 6is for explanation only and not for limitation. Other topologies as willoccur to those of skill in the art may also be useful according toembodiments of the present invention.

Each compute node 0-15 has four processors, or processing cores, labeledP0, P1, P2, and P3. The processors of each compute node 0-15 are capableof operating independently for serial processing among the processorsP0-P3 and capable of operating symmetrically for parallel processingamong the processors P0-P3. In the example of FIG. 6, a service node(not shown) boots up a first subset (611) of nodes that includes nodes0-7 of racks 0-1 in a serial processing mode (610). The service nodealso boots up a second subset (613) of nodes that includes nodes 8-15 ofracks 2-3 in a parallel processing mode (612).

In the example of FIG. 6, the parallel computer (100) executes anapplication (158) on the computer's compute nodes. The application (158)of FIG. 6 has both parallel segments (601, 603, 605) designated forparallel processing and serial segments (600, 602, 604) designated forserial processing. The serial segments (600, 602, 604) include computerprogram instructions for execution serially in a single thread, whilethe parallel segments (601, 603, 605) includes computer programinstructions for execution among multiple threads in parallel. Theapplication (158) of FIG. 6 may distinguish the serial segments (600,602, 604) from the parallel segment (601, 603, 605) in the application(158) using the programming directive ‘#pragma omp parallel {. . . }’for each parallel segment (601, 603, 605) such that the computer programinstructions of each parallel segment (601, 603, 605) are placed in thecurly braces of the directive. Readers will note that such an exemplarydirective is for explanation only and not for limitation. In fact, theserial segments (600, 602, 604) may be distinguished from the parallelsegment (601, 603, 605) in many other ways as will occur to those ofskill in the art such as, for example, processor operation codes,historical execution information, and so on.

In the example of FIG. 6, the service node profiles the application(158) to identify the serial segments (600, 602, 604) and the parallelsegments (601, 603, 605) of the application (158) prior to applicationdeployment on the parallel computer (100). The service node may profilethe application (158) by parsing application instructions of theapplication (158) for serial segments (600, 602, 604) and parallelsegments (601, 603, 605). That is, the service node may examine theapplication (158) for directives such as ‘#pragma omp parallel’ orspecial processor operation codes specifying that a particular segmentof application instructions is designated for serial processing orparallel processing. In some other embodiments, the service node mayprofile the application (158) by identifying the serial segments (600,602, 604) and the parallel segments (601, 603, 605) of the application(158) in dependence upon historical execution information. Regardless ofthe manner in which the application's serial segments (600, 602, 604)and application's parallel segments (601, 603, 605) are identified, theservice node uses the application's profile to deploy the application(158) for execution on the parallel computer (100).

In the example of FIG. 6, the distribution of the serial segments (600,602, 604) and the parallel segments (601, 603, 605) along theapplication's execution sequence (620) is described by an interleaverate for the application (158). That is, the interleave rate describesthe frequency with which segments designated for serial processing andsegments designated for parallel processing alternate during theapplication's execution sequence (620). Readers will note that theinterleave rate between the serial segments (600, 602, 604) and theparallel segments (601, 603, 605) of the application (158) is high.Because the application's serial segment (600, 602, 604) are executed onnodes booted up in serial processing mode (610) and the application'sparallel segments (601, 603, 605) are executed on nodes booted up inparallel processing mode (612), the parallel computer (100) will have tofrequently migrate the application (158) of FIG. 6 between the nodesbooted up in serial processing mode (610) and the nodes booted up inparallel processing mode (612).

In the example of FIG. 6, a service node deploys the application (158)for execution on the parallel computer (100) based on the application'sprofile, which identifies serial segments (600, 602, 604) and parallelsegments (601, 603, 605), and based on the proximity within the datacommunications network of the compute nodes 0-7 in the first subset(611) relative to the compute nodes 8-15 in the second subset (613). Theservice node may utilize the application's profile to determine that theinterleave rate for the application (158) illustrated in FIG. 6 is high,thereby indicating that the application will require frequent migrationbetween nodes booted up in serial processing mode and nodes booted up inparallel processing mode. Because the parallel computer (100) willlikely migrate the application (158) frequently during execution, theservice node selects particular compute nodes in the first subset (611)to process the application's serial segments and particular computenodes in the second subset (613) to process the application's parallelsegments such that the selected compute nodes have predefined locationsin the data communications network to increase the proximity of theselected compute nodes in the first subset (611) and the selectedcompute nodes in the second subset (613). In the example of FIG. 6, thecompute nodes in the first subset (611) and the second subset (613) atlocations in the network to increase proximity are the compute nodes 4-7in rack 1 and compute nodes 8-11 in rack 2. As such, the service nodeselects compute nodes in rack 1 and rack 2 for processing segments theapplication (158).

For discussion purposes with respect to FIG. 6, let us consider that anapplication developer or a system administrator has decided that fourinstances of the application (158) will be executed on the parallelcomputer (100). That is, four instances of the application (158) will beprocessed concurrently using four processors of the parallel computer(100). Accordingly, the serial segments (600, 602, 604) of theapplication (158) will be executed using a minimum of four threads. Thatis, during serial segments, the parallel computer (100) will processfour instances of the application (158), each instance utilizing asingle thread of execution. Additional threads, however, may be utilizedduring parallel segments (601, 603, 605) of the application (158) aseach of those four initial threads spawn threads for enhancedperformance during those parallel segments (601, 603, 605). From theabove description, readers will note that during serial segments of theapplication a certain level of parallel processing is being performed,but during the parallel segments of the application where additionalthreads are spawned, an even greater level of parallel processing may beutilized to enhance performance.

Because the application (158) begins with a serial segment (600), aservice node initially configures four instances of the application(158) on four processors on a single compute nodes booted up in serialprocessing mode (610). Specifically, the service node configures theapplication (158) on each processor P0-P3 of compute node 4 in rack 1 asindicated by the shading of each of those processors. Because eachinstance of the application (158) only uses one thread during the serialsegments (600, 602, 604), the application (158) only uses fourprocessing cores for execution, those four processing cores processingthe four instances independently of one another. Readers will note thatbecause all processors P0-P3 on compute node 4 are utilized forprocessing the serial segment (600), the processing resources of thecompute node 4 are not squandered.

While all of the processors P0-P3 of compute node 4 are being utilizedfor execution of the application (158), no additional processors areavailable on node 4 to process threads spawned when a parallel segment(601, 603, 605) of the application (158) is encountered. Uponencountering the parallel segment (601, 603, 605) during theapplication's execution sequence (620), therefore the parallel computer(100) migrates the application (158) to the compute nodes of rack 2booted up in a parallel processing mode (612). Specifically in theexample of FIG. 6, the following migration occurs: the applicationinstance on P0 of node 4 is migrated to P0 of node 8; the applicationinstance on P1 of node 4 is migrated to P0 of node 9; the applicationinstance on P2 of node 4 is migrated to P0 of node 10; and theapplication instance on P3 of node 4 is migrated to P0 of node 11.Again, readers will recall that the application is migrated to theadjacent nodes in the data communications network because of the highinterleave rate between the application's serial segments and parallelsegments. Because nodes 8-11 are booted in parallel processing mode(612), P0 serves as a master processor and the remaining processors P1-3serve as slave processors to P0. During execution of the parallelsegment (601) of FIG. 6, therefore, each application instance on P0 ofnodes 8-11 may spawn threads to processors P1-3 of each node to aid inprocessing the parallel segment (601) as represented in FIG. 6 usingarrows from P0 to P1-3 in nodes 9-11.

While all of the processors P0-3 of each compute node 8-11 are beingutilized for execution of the application (158), none of the processorsare underutilized because each processor executes either the main threadof an instance of the application (158) or a thread spawned from themain thread. Executing the serial segments (600, 602, 604) of theapplication (158) on compute node 8-11, however, would result in threeprocessors P1-3 not being utilized. Upon encountering the serialsegments (600, 602, 604) during execution sequence (620) of theapplication (158) in the example of

FIG. 6, therefore, the parallel computer (100) migrates the application(158) to the compute node 4 booted up in the serial processing mode(610).

In the application (158) illustrated in FIG. 6, the serial segments andthe parallel segments are distributed along the application's executionsequence such that the application frequently alternates those segmentsdesignated for serial processing and those segments designated forparallel processing. Accordingly, the application is deployed on computenodes booted up in serial processing mode and compute nodes booted up inparallel processing mode that are in close proximity to one another. Inmany embodiments, however, an application may not frequently alternatebetween segments designated for serial processing and segmentsdesignated for parallel processing during the application executionsequence. For further explanation, consider FIG. 7 that sets forth aline drawing illustrating a further exemplary parallel computer on whichan application is configured for execution according to embodiments ofthe present invention.

The parallel computer (100) of FIG. 7 includes sixteen compute nodeslabeled 0-15 and connected together through a data communicationsnetwork. The compute nodes are spread across four racks labeled 0-3.Rack 0 includes nodes 0-3, rack 1 includes nodes 4-7, rack 2 includes8-11, and rack 3 includes nodes 12-15. Due to the physical links in thedata communications network connecting the nodes in each rack 0-3, thenodes in rack 0 are directly connected to the nodes in rack 1, the nodesin rack 1 are directly connected to the nodes in rack 2, and the nodesin rack 2 are directly connected to the nodes in rack 3. All other datacommunication connections between racks 0-3 are indirect and requiretraversal of an intervening rack. For example, data communicationsbetween the nodes in rack 0 and the nodes in rack 2 must pass throughrack 1 in the example of FIG. 7. Readers will note, however, that thenetwork topology of the nodes in the example of FIG. 7 is forexplanation only and not for limitation.

Each compute node 0-15 has four processors, or processing cores, labeledP0, P1, P2, and P3. The processors of each compute node 0-15 are capableof operating independently for serial processing among the processorsP0-P3 and capable of operating symmetrically for parallel processingamong the processors P0-P3. In the example of FIG. 7, a service node(not shown) boots up a first subset (611) of nodes that includes nodes0-7 of racks 0-1 in a serial processing mode (610). The service nodealso boots up a second subset (613) of nodes that includes nodes 8-15 ofracks 2-3 in a parallel processing mode (612).

In the example of FIG. 7, the parallel computer (100) executes anapplication (158) on the computer's compute nodes. The application (158)of FIG. 7 has both parallel segments (703, 704, 705) designated forparallel processing and serial segments (700, 701, 702) designated forserial processing. The serial segments (700, 701, 702) include computerprogram instructions for execution serially in a single thread, whilethe parallel segments (703, 704, 705) includes computer programinstructions for execution among multiple threads in parallel. Theapplication (158) of FIG. 7 may distinguish the serial segments (700,701, 702) from the parallel segment (703, 704, 705) in the application(158) using the programming directive ‘#pragma omp parallel {. . . }’for each parallel segment (703, 704, 705) such that the computer programinstructions of each parallel segment (703, 704, 705) are placed in thecurly braces of the directive. Readers will note that such an exemplarydirective is for explanation only and not for limitation. In fact, theserial segments (700, 701, 702) may be distinguished from the parallelsegment (703, 704, 705) in many other ways as will occur to those ofskill in the art such as, for example, processor operation codes,historical execution information, and so on.

In the example of FIG. 7, the service node profiles the application(158) to identify the serial segments (700, 701, 702) and the parallelsegments (703, 704, 705) of the application (158) prior to applicationdeployment on the parallel computer (100). As mentioned above, theservice node may profile the application (158) by parsing applicationinstructions of the application (158) for serial segments (700, 701,702) and parallel segments (703, 704, 705) or by identifying the serialsegments (700, 701, 702) and the parallel segments (703, 704, 705) ofthe application (158) in dependence upon historical executioninformation. Regardless of the manner in which the application's serialsegments (700, 701, 702) and application's parallel segments (703, 704,705) are identified, the service node uses the application's profile todeploy the application (158) for execution on the parallel computer(100).

Based on the application's profile, a service node of the parallelcomputer (100) in the example of FIG. 7 determines that theapplication's interleave rate between the serial segments (700, 701,702) and the parallel segments (703, 704, 705) is low. Readers willrecall from above that an application's interleave rate describes thedistribution of the application's serial segments and parallel segmentsalong the application's execution sequence (620). That is, theinterleave rate describes the frequency with which segments designatedfor serial processing and segments designated for parallel processingalternate during the application's execution sequence (620). Even thoughthe application's serial segment (700, 701, 702) are typically executedon nodes booted up in serial processing mode (610) and the application'sparallel segments (703, 704, 705) are typically executed on nodes bootedup in parallel processing mode (612), the parallel computer (100) willonly have to migrate the application (158) of FIG. 7 once between thenodes booted up in serial processing mode (610) and the nodes booted upin parallel processing mode (612). Only one migration is requiredbecause only once during the execution sequence (620) does theapplication (158) switch from a serial segment to a parallel segment orvice versa.

In the example of FIG. 7, a service node deploys the application (158)for execution on the parallel computer (100) based on the application'sprofile, which identifies serial segments (700, 701, 702) and parallelsegments (703, 704, 705), and based on the proximity within the datacommunications network of the compute nodes 0-7 in the first subset(611) relative to the compute nodes 8-15 in the second subset (613). Theservice node may utilize the application's profile to determine that theinterleave rate for the application (158) illustrated in FIG. 7 is low,thereby indicating that the application will not require frequentmigration between nodes booted up in serial processing mode and nodesbooted up in parallel processing mode. Because the parallel computer(100) will not typically migrate the application (158) of FIG. 7frequently during execution, the service node selects particular computenodes in the first subset (611) to process the application's serialsegments and particular compute nodes in the second subset (613) toprocess the application's parallel segments such that the selectedcompute nodes have predefined locations in the data communicationsnetwork to reduce the proximity of the selected compute nodes in thefirst subset (611) and the selected compute nodes in the second subset(613). In the example of FIG. 7, the compute nodes in the first subset(611) and the second subset (613) at locations in the network to reduceproximity are the compute nodes 0-3 in rack 0 and compute nodes 12-15 inrack 3. As such, the service node selects compute nodes in rack 0 andrack 3 for processing segments the application (158).

For discussion purposes with respect to FIG. 7, let us consider that anapplication developer or a system administrator has decided that fourinstances of the application (158) will be executed on the parallelcomputer (100). That is, four instances of the application (158) will beprocessed concurrently using four processors of the parallel computer(100). Accordingly, the serial segments (700, 701, 702) of theapplication (158) will be executed using a minimum of four threads. Thatis, during serial segments, the parallel computer (100) will processfour instances of the application (158), each instance utilizing asingle thread of execution. Additional threads, however, may be utilizedduring parallel segments (703, 704, 705) of the application (158) aseach of those four initial threads spawn threads for enhancedperformance during those parallel segments (703, 704, 705). From theabove description, readers will note that during serial segments of theapplication a certain level of parallel processing is being performed,but during the parallel segments of the application where additionalthreads are spawned, an even greater level of parallel processing may beutilized to enhance performance.

Because the application (158) begins with a serial segment (600), aservice node initially configures four instances of the application(158) on four processors on a single compute nodes booted up in serialprocessing mode (610). Specifically, the service node configures theapplication (158) on each processor P0-P3 of compute node 0 in rack 0 asindicated by the shading of each of those processors. Because eachinstance of the application (158) only uses one thread during the serialsegments (700, 701, 702), the application (158) only uses fourprocessing cores for execution, those four processing cores processingthe four instances independently of one another. Readers will note thatbecause all processors P0-P3 on compute node 0 are utilized forprocessing the serial segment (700), the processing resources of thecompute node 0 are not squandered.

While all of the processors P0-P3 of compute node 0 are being utilizedfor execution of the application (158), no additional processors areavailable on node 0 to process threads spawned when a parallel segment(703, 704, 705) of the application (158) is encountered. Uponencountering the parallel segment (703, 704, 705) during theapplication's execution sequence (620), therefore the parallel computer(100) migrates the application (158) to the compute nodes of rack 3booted up in a parallel processing mode (612). Specifically in theexample of FIG. 7, the following migration occurs: the applicationinstance on P0 of node 0 is migrated to P0 of node 12; the applicationinstance on P1 of node 0 is migrated to P0 of node 13; the applicationinstance on P2 of node 0 is migrated to P0 of node 14; and theapplication instance on P3 of node 0 is migrated to P0 of node 15.Because nodes 12-15 are booted in parallel processing mode (612), P0serves as a master processor and the remaining processors P1-3 serve asslave processors to P0. During execution of the parallel segments (703,704, 705) of FIG. 7, therefore, each application instance on P0 of nodes12-15 may spawn threads to processors P1-3 of each node to aid inprocessing the parallel segments (703, 704, 705) as represented in FIG.7 using arrows from P0 to P1-3 in nodes 12-15.

Readers will note that, in the example of FIG. 7, the application (158)is migrated between nodes at disparate ends of the data communicationsnetwork because of the low interleave rate between the application'sserial segments and parallel segments. Migrating the application (158)of FIG. 7 between nodes at disparate ends of the data communicationsnetwork because of the low interleave rate between the application'sserial segments and parallel segments advantageously leaves the nodes inracks 1 and 2 available to process applications having a higherinterleave rate between segments designated for serial processing andfor parallel processing.

For further explanation, FIG. 8 sets forth a flow chart illustrating anexemplary method for configuring an application for execution on aparallel computer according to the present invention. The parallelcomputer described with reference to FIG. 8 includes plurality ofcompute nodes connected together through a data communications network.Each compute node has a plurality of processors capable of operatingindependently for serial processing among the processors and capable ofoperating symmetrically for parallel processing among the processors.The application (158) of FIG. 8 has parallel segments (812) designatedfor parallel processing and serial segments (810) designated for serialprocessing.

The method of FIG. 8 includes booting up (800) a first subset (802) ofthe plurality of compute nodes in a serial processing mode. Booting up(800) a first subset (802) of the plurality of compute nodes in a serialprocessing mode according to the method of FIG. 8 may be carried out bysetting register values in processors, memory, or bus circuitry of eachcompute node in the first subset (802) to instruct the node to operatein a serial processing mode. The minimum number of nodes included in thefirst subset (802) may be determined by dividing the number of instancesof the application (158) that the application developer or systemadministrator desires to run concurrently on the parallel computer bythe number of processors on each compute node. For example, if anapplication developer or a system administrator desires to have theparallel computer execute sixteen instances of the application (158)concurrently and each compute node has four processors, then the firstsubset (802) of nodes should include at least four compute nodes. Inmany embodiments, however, a service node may boot all of the computenodes included in several racks in serial processing mode to provide agroup of compute nodes for processing multiple applicationsconcurrently.

The method of FIG. 8 also includes booting up (804) a second subset(806) of the plurality of compute nodes in a parallel processing mode.Booting up (804) a second subset (806) of the plurality of compute nodesin a parallel processing mode according to the method of FIG. 8 may becarried out by setting register values in processors, memory, or buscircuitry of each compute node in the second subset (806) to instructthe node to operate in a parallel processing mode. The minimum number ofnodes included in the second subset (806) may be determined to be thenumber of instances of the application (158) that the applicationdeveloper or system administrator desires to run concurrently on theparallel computer by the number of processors on each compute node. Forexample, if an application developer or a system administrator desire tohave the parallel computer execute sixteen instances of the application(158) concurrently, then the second subset (806) of nodes may include atleast sixteen compute nodes. In many embodiments, however, a servicenode may boot all of the compute nodes included in several racks inparallel processing mode to provide a group of compute nodes forprocessing multiple applications concurrently.

The method of FIG. 8 includes profiling (808), prior to applicationdeployment on the parallel computer, the application (158) to identifythe serial segments (810) and the parallel segments (812) of theapplication (158). Profiling (808), prior to application deployment onthe parallel computer, the application (158) to identify the serialsegments (810) and the parallel segments (812) of the application (158)according to the method of FIG. 8 may be carried out by a service nodefor the parallel computer or one or more compute nodes. The applicationprofile (814) of FIG. 8 is a data structure that specifies the serialsegments (810) and the parallel segments (812) in the application'sexecution sequence. The application profile (814) may specify the serialsegments (810) and the parallel segments (812) using processorinstructions counter values that denote the beginning and the end of theserial segments (810) and the parallel segments (812). Readers willnote, however, that such an implementation of an exemplary applicationprofile (814) is for explanation only and not for limitation. Otherimplementations of an application profile as will occur to those ofskill in the art may also be useful in configuring an application forexecution on a parallel computer according to embodiments of the presentinvention.

Profiling (808), prior to application deployment on the parallelcomputer, the application (158) to identify the serial segments (810)and the parallel segments (812) of the application (158) according tothe method of FIG. 8 may be carried out by parsing applicationinstructions of the application (158) for serial segments (810) andparallel segments (812). For example, a OpenMP™ directive ‘#pragma ompparallel {. . . }’ specifies that all of the instructions in the curlybraces may be executed in parallel using multiple threads that sharedthe same memory. For another example, consider the UNIX instruction‘thr_create( )’ that invokes a function to create a thread that executeconcurrently with the thread calling the function.

Profiling (808), prior to application deployment on the parallelcomputer, the application (158) to identify the serial segments (810)and the parallel segments (812) of the application (158) according tothe method of FIG. 8 may also be carried out by identifying the serialsegments (810) and the parallel segments (812) of the application (158)in dependence upon historical execution information. The historicalexecution information may be gathered by a service node that administersthe application's execution on the parallel computer. Using historicalexecution information, the parallel computer may determined at whatpoints during the application's execution that the application waspreviously migrated between nodes booted up in serial processing modeand nodes booted up in parallel processing mode. The parallel computermay use the identified points during the application's execution thatthe application was previously migrated between nodes booted up inserial processing mode and nodes booted up in parallel processing modeto infer the serial segments (810) and parallel segments (812) of theapplication (158).

The method of FIG. 8 also includes deploying (816) the application (158)for execution on the parallel computer in dependence upon the profile(814) of the application (158) and proximity within the datacommunications network of the compute nodes in the first subset (802)relative to the compute nodes in the second subset (806). Deploying(816) the application (158) for execution on the parallel computeraccording to the method of FIG. 8 may be carried out by a service nodeof the parallel computer or one or more compute nodes for the parallelcomputer.

Deploying (816) the application (158) for execution on the parallelcomputer according to the method of FIG. 8 includes determining (818) aninterleave rate (820) for the application (158) in dependence upon theprofile (814) for the application (158). The interleave rate (820) ofFIG. 8 specifies the distribution of the serial segments (810) and theparallel segments (812) along the application's execution sequence. Aservice node or one or more compute nodes may determine (818) aninterleave rate (820) for the application (158) according to the methodof FIG. 8 by calculating the number of times specified by theapplication profile (814) that the application's execution sequenceswitches between a serial segment and a parallel segment. For example,consider an application profile for an application having the followingexemplary execution sequence:

-   -   serial segment    -   parallel segment    -   serial segment    -   parallel segment    -   serial segment    -   parallel segment    -   serial segment

In the example above, the number of times that the application'sexecution sequence switches between a serial segment and a parallelsegment is six times. The application having the exemplary executionsequence above therefore may be assigned an interleave rate of six timesper execution.

For further explanation, consider an application profile for anapplication having the following exemplary execution sequence:

-   -   serial segment    -   serial segment    -   serial segment    -   serial segment    -   parallel segment    -   parallel segment    -   parallel segment

In the example above, the number of times that the application'sexecution sequence switches between a serial segment and a parallelsegment is one time. The application having the exemplary executionsequence above therefore may be assigned an interleave rate of one timeper execution.

Deploying (816) the application (158) for execution on the parallelcomputer according to the method of FIG. 8 also includes determining(824) whether the interleave rate (820) for the application (158)exceeds a particular threshold (822). The particular threshold (822)represents a value for interleave rates above which executionperformance for an application is reduced when the nodes in serial andparallel processing mode are not in close proximity to one another inthe data communications network. An application's interleave rate (820)that exceeds the threshold (822) indicates that the application migratesback and forth between nodes in serial and parallel processing modefrequently enough during execution that the overhead of migrating theapplication is generally only worthwhile from a performance standpointif the application does not have to migrate very far across the network.The threshold (822) of FIG. 8 may be specific to a particularapplication or may be used across many applications. Moreover, thethreshold (822) may be statically set by a system administrator ordynamically determined based on network performance metrics.

In the method of FIG. 8, deploying (816) the application (158) forexecution on the parallel computer also includes selecting (826), if theinterleave rate (820) for the application (158) exceeds the particularthreshold (822), particular compute nodes in the first subset (802) toprocess the application's serial segments (810) and particular computenodes in the second subset (806) to process the application's parallelsegments (812) such that the selected compute nodes have predefinedlocations in the data communications network to increase the proximityof the selected compute nodes in the first subset (802) and the selectedcompute nodes in the second subset (806). Selecting (826) compute nodesin the first and second subsets (802, 806) that have predefinedlocations in the data communications network to increase the proximityof the selected compute nodes in the first subset (802) and the selectedcompute nodes in the second subset (806) according to the method of FIG.8 may be carried out by determining which nodes closest to the networkboundary between the nodes booted in serial processing mode and thenodes booted in parallel processing mode are available for processingand selecting those nodes for executing the application (158).

Deploying (816) the application (158) for execution on the parallelcomputer according to the method of FIG. 8 then includes configuring(830) the application (158) for execution on the selected compute nodes.In the example of FIG. 8, readers will note that the nodes selected inthe parallel computer (840) are the nodes that have predefined locationsin the data communications network to increase the proximity of theselected compute nodes in serial processing mode (610) and the selectedcompute nodes in parallel processing mode (612). That is, the selectednodes are the closest nodes available to the network boundary in theparallel computer (840) between the nodes in serial processing mode andthe nodes in parallel processing mode. FIG. 8 illustrates the selectednodes in parallel computer (840) through use of the blacked squares,which represent processors on the selected nodes in a manner similar tothe representation on FIG. 6.

Deploying (816) the application (158) for execution on the parallelcomputer according to the method of FIG. 8 includes selecting (828), ifthe interleave rate (820) for the application (158) does not exceed theparticular threshold (822), particular compute nodes in the first subset(802) to process the application's serial segments (810) and particularcompute nodes in the second subset (806) to process the application'sparallel segments (812) such that the selected compute nodes havepredefined locations in the data communications network to reduce theproximity of the selected compute nodes in the first subset (802) andthe selected compute nodes in the second subset (806). Selecting (828)compute nodes in the first and second subset (802, 806) that havepredefined locations in the data communications network to reduce theproximity of the selected compute nodes in the first subset (802) andthe selected compute nodes in the second subset (806) according to themethod of FIG. 8 may be carried out by determining which nodes farthestfrom the network boundary between the nodes booted in serial processingmode and the nodes booted in parallel processing mode are available forprocessing and selecting those nodes for executing the application(158).

Deploying (816) the application (158) for execution on the parallelcomputer according to the method of FIG. 8 then includes configuring(832) the application (158) for execution on the selected compute nodes.In the example of FIG. 8, readers will note that the nodes selected inthe parallel computer (842) are the nodes that have predefined locationsin the data communications network to reduce the proximity of theselected compute nodes in serial processing mode (610) and the selectedcompute nodes in parallel processing mode (612). That is, the selectednodes are the farthest nodes available from the network boundary in theparallel computer (842) between the nodes in serial processing mode andthe nodes in parallel processing mode. FIG. 8 illustrates the selectednodes in parallel computer (842) through use of the blacked squares,which represent processors on the selected nodes in a manner similar tothe representation on FIG. 7.

Exemplary embodiments of the present invention are described largely inthe context of a fully functional parallel computer system for providingnearest neighbor point-to-point communications among compute nodes of anoperational group in a global combining network. Readers of skill in theart will recognize, however, that the present invention also may beembodied in a computer program product disposed on computer readablemedia for use with any suitable data processing system. Such computerreadable media may be transmission media or recordable media formachine-readable information, including magnetic media, optical media,or other suitable media. Examples of recordable media include magneticdisks in hard drives or diskettes, compact disks for optical drives,magnetic tape, and others as will occur to those of skill in the art.Examples of transmission media include telephone networks for voicecommunications and digital data communications networks such as, forexample, Ethernets™ and networks that communicate with the InternetProtocol and the World Wide Web as well as wireless transmission mediasuch as, for example, networks implemented according to the IEEE 802.11family of specifications. Persons skilled in the art will immediatelyrecognize that any computer system having suitable programming meanswill be capable of executing the steps of the method of the invention asembodied in a program product. Persons skilled in the art will recognizeimmediately that, although some of the exemplary embodiments describedin this specification are oriented to software installed and executingon computer hardware, nevertheless, alternative embodiments implementedas firmware or as hardware are well within the scope of the presentinvention.

It will be understood from the foregoing description that modificationsand changes may be made in various embodiments of the present inventionwithout departing from its true spirit. The descriptions in thisspecification are for purposes of illustration only and are not to beconstrued in a limiting sense. The scope of the present invention islimited only by the language of the following claims.

1. A method of configuring an application for execution on a parallelcomputer, the parallel computer comprising a plurality of compute nodesconnected together through a data communications network, each computenode having a plurality of processors capable of operating independentlyfor serial processing among the processors and capable of operatingsymmetrically for parallel processing among the processors, theapplication having parallel segments designated for parallel processingand serial segments designated for serial processing, the methodcomprising: profiling, prior to application deployment on the parallelcomputer, the application to identify the serial segments and theparallel segments of the application; and deploying the application forexecution on the parallel computer in dependence upon the profile of theapplication and proximity within the data communications network ofcompute nodes in a first subset of compute nodes configured in a serialprocessing mode relative to compute nodes in a second subset of computenodes configured in a parallel processing mode.
 2. The method of claim 1wherein deploying the application for execution on the parallel computerin dependence upon the profile of the application and proximity withinthe data communications network of the compute nodes in the first subsetrelative to the compute nodes in the second subset further comprises:determining an interleave rate for the application in dependence uponthe profile for the application, the interleave rate specifying thedistribution of the serial segments and the parallel segments along theapplication's execution sequence; determining whether the interleaverate for the application exceeds a particular threshold; selecting, ifthe interleave rate for the application exceeds the particularthreshold, particular compute nodes in the first subset to process theapplication's serial segments and particular compute nodes in the secondsubset to process the application's parallel segments such that theselected compute nodes have predefined locations in the datacommunications network to increase the proximity of the selected computenodes in the first subset and the selected compute nodes in the secondsubset; and configuring the application for execution on the selectedcompute nodes.
 3. The method of claim 1 wherein deploying theapplication for execution on the parallel computer in dependence uponthe profile of the application and proximity within the datacommunications network of the compute nodes in the first subset relativeto the compute nodes in the second subset further comprises: determiningan interleave rate for the application in dependence upon the profilefor the application, the interleave rate specifying the distribution ofthe serial segments and the parallel segments along the application'sexecution sequence; determining whether the interleave rate for theapplication does not exceed a particular threshold; selecting, if theinterleave rate for the application does not exceed the particularthreshold, particular compute nodes in the first subset to process theapplication's serial segments and particular compute nodes in the secondsubset to process the application's parallel segments such that theselected compute nodes have predefined locations in the datacommunications network to reduce the proximity of the selected computenodes in the first subset and the selected compute nodes in the secondsubset; and configuring the application for execution on the selectedcompute nodes.
 4. The method of claim 1 wherein profiling, prior toapplication deployment on the parallel computer, the application toidentify the serial segments and the parallel segments of theapplication further comprises parsing application instructions of theapplication for serial segments and parallel segments.
 5. The method ofclaim 1 wherein profiling, prior to application deployment on theparallel computer, the application to identify the serial segments andthe parallel segments of the application further comprises identifyingthe serial segments and the parallel segments of the application independence upon historical execution information.
 6. The method of claim1 wherein the plurality of compute nodes are connected together througha plurality of data communications networks, at least one of the datacommunications networks optimized for point to point datacommunications, at least one of the data communications networksoptimized for collective operations.
 7. A parallel computer forconfiguring an application for execution on a parallel computer, theparallel computer comprising a plurality of compute nodes connectedtogether through a data communications network, each compute node havinga plurality of processors capable of operating independently for serialprocessing among the processors and capable of operating symmetricallyfor parallel processing among the processors, the application havingparallel segments designated for parallel processing and serial segmentsdesignated for serial processing, the parallel computer comprisingcomputer memory operatively coupled to the processors of the pluralityof compute nodes, the computer memory having disposed within it computerprogram instructions capable of: profiling, prior to applicationdeployment on the parallel computer, the application to identify theserial segments and the parallel segments of the application; anddeploying the application for execution on the parallel computer independence upon the profile of the application and proximity within thedata communications network of compute nodes in a first subset ofcompute nodes configured in a serial processing mode relative to computenodes in a second subset of compute nodes configured in a parallelprocessing mode.
 8. The parallel computer of claim 7 wherein deployingthe application for execution on the parallel computer in dependenceupon the profile of the application and proximity within the datacommunications network of the compute nodes in the first subset relativeto the compute nodes in the second subset further comprises: determiningan interleave rate for the application in dependence upon the profilefor the application, the interleave rate specifying the distribution ofthe serial segments and the parallel segments along the application'sexecution sequence; determining whether the interleave rate for theapplication exceeds a particular threshold; selecting, if the interleaverate for the application exceeds the particular threshold, particularcompute nodes in the first subset to process the application's serialsegments and particular compute nodes in the second subset to processthe application's parallel segments such that the selected compute nodeshave predefined locations in the data communications network to increasethe proximity of the selected compute nodes in the first subset and theselected compute nodes in the second subset; and configuring theapplication for execution on the selected compute nodes.
 9. The parallelcomputer of claim 7 wherein deploying the application for execution onthe parallel computer in dependence upon the profile of the applicationand proximity within the data communications network of the computenodes in the first subset relative to the compute nodes in the secondsubset further comprises: determining an interleave rate for theapplication in dependence upon the profile for the application, theinterleave rate specifying the distribution of the serial segments andthe parallel segments along the application's execution sequence;determining whether the interleave rate for the application does notexceed a particular threshold; selecting, if the interleave rate for theapplication does not exceed the particular threshold, particular computenodes in the first subset to process the application's serial segmentsand particular compute nodes in the second subset to process theapplication's parallel segments such that the selected compute nodeshave predefined locations in the data communications network to reducethe proximity of the selected compute nodes in the first subset and theselected compute nodes in the second subset; and configuring theapplication for execution on the selected compute nodes.
 10. Theparallel computer of claim 7 wherein profiling, prior to applicationdeployment on the parallel computer, the application to identify theserial segments and the parallel segments of the application furthercomprises: parsing application instructions of the application forserial segments and parallel segments.
 11. The parallel computer ofclaim 7 wherein profiling, prior to application deployment on theparallel computer, the application to identify the serial segments andthe parallel segments of the application further comprises: identifyingthe serial segments and the parallel segments of the application independence upon historical execution information.
 12. The parallelcomputer of claim 7 wherein the plurality of compute nodes are connectedtogether through a plurality of data communications networks, at leastone of the data communications networks optimized for point to pointdata communications, at least one of the data communications networksoptimized for collective operations.
 13. A computer program product forconfiguring an application for execution on a parallel computer, theparallel computer comprising a plurality of compute nodes connectedtogether through a data communications network, each compute node havinga plurality of processors capable of operating independently for serialprocessing among the processors and capable of operating symmetricallyfor parallel processing among the processors, the application havingparallel segments designated for parallel processing and serial segmentsdesignated for serial processing, the computer program product disposedupon a recordable computer readable medium, the computer program productcomprising computer program instructions capable of: profiling, prior toapplication deployment on the parallel computer, the application toidentify the serial segments and the parallel segments of theapplication; and deploying the application for execution on the parallelcomputer in dependence upon the profile of the application and proximitywithin the data communications network of compute nodes in a firstsubset of compute nodes configured in a serial processing mode relativeto compute nodes in a second subset of compute nodes configured in aparallel processing mode.
 14. The computer program product of claim 13wherein deploying the application for execution on the parallel computerin dependence upon the profile of the application and proximity withinthe data communications network of the compute nodes in the first subsetrelative to the compute nodes in the second subset further comprises:determining an interleave rate for the application in dependence uponthe profile for the application, the interleave rate specifying thedistribution of the serial segments and the parallel segments along theapplication's execution sequence; determining whether the interleaverate for the application exceeds a particular threshold; selecting, ifthe interleave rate for the application exceeds the particularthreshold, particular compute nodes in the first subset to process theapplication's serial segments and particular compute nodes in the secondsubset to process the application's parallel segments such that theselected compute nodes have predefined locations in the datacommunications network to increase the proximity of the selected computenodes in the first subset and the selected compute nodes in the secondsubset; and configuring the application for execution on the selectedcompute nodes.
 15. The computer program product of claim 13 whereindeploying the application for execution on the parallel computer independence upon the profile of the application and proximity within thedata communications network of the compute nodes in the first subsetrelative to the compute nodes in the second subset further comprises:determining an interleave rate for the application in dependence uponthe profile for the application, the interleave rate specifying thedistribution of the serial segments and the parallel segments along theapplication's execution sequence; determining whether the interleaverate for the application does not exceed a particular threshold;selecting, if the interleave rate for the application does not exceedthe particular threshold, particular compute nodes in the first subsetto process the application's serial segments and particular computenodes in the second subset to process the application's parallelsegments such that the selected compute nodes have predefined locationsin the data communications network to reduce the proximity of theselected compute nodes in the first subset and the selected computenodes in the second subset; and configuring the application forexecution on the selected compute nodes.
 16. The computer programproduct of claim 13 wherein profiling, prior to application deploymenton the parallel computer, the application to identify the serialsegments and the parallel segments of the application further comprisesparsing application instructions of the application for serial segmentsand parallel segments.
 17. The computer program product of claim 13wherein profiling, prior to application deployment on the parallelcomputer, the application to identify the serial segments and theparallel segments of the application further comprises identifying theserial segments and the parallel segments of the application independence upon historical execution information.
 18. The computerprogram product of claim 13 wherein the plurality of compute nodes areconnected together through a plurality of data communications networks,at least one of the data communications networks optimized for point topoint data communications, at least one of the data communicationsnetworks optimized for collective operations.
 19. (canceled) 20.(canceled)